A Watershed-Scale Groundwater-Land-Surface Model · 2011-10-13 · A Watershed-Scale...
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A Watershed-Scale Groundwater-Land-Surface Model Yuning Shi*, Kenneth Davis*, Christopher Duffy † , Xuan Yu † *Department of Meteorology, the Pennsylvania State University † Department of Civil and Environmental Engineering, the Pennsylvania State University Introduction Model and data • A land-surface module is incorporated into the Penn State Integrated Hydrologic Model 2.0 (PIHM 2.0) –Fully coupled surface water, groundwater, and land-surface components – Land-surface scheme is mainly adapted from the Noah LSM • Susquehanna/Shale Hills Observatory (SSHO) in central Pennsylvania (0.08 km 2 ) –Small-scale V-shaped catchment with 1 st order stream –Real Time Hydrologic monitoring network (RTHnet) with an array of land-surface and sub-surface sensors is installed in SSHO Future Work • Questions to answer: – How does land-surface affect hydrologic predictions? – How does groundwater improve SEB prediction? • Incorporate data assimilation module into model to assimilate in- situ measurements and optimize model parameters • Study subsurface-land-surface interaction • Test model on different spatial scales • Evaluate model on flood/drought prediction at scales up to the Juniata River Basin (~8800 km 2 ) Land Surface Models - Ignore deep ground- water and lateral water flow Groundwater models - Relatively simple evapo-transpiration scheme Coupled models of groundwater and land surface - may yield significant improvements in short- term climate and flood/drought forecasting Map of sensible heat flux (left) and latent heat fluxes (right) averaged over entire simulation period Results Simulated sensible heat flux, latent heat flux, ground heat flux (G), and surface skin temperature (T skin ) as functions of water table depth Comparison of hourly water table depth (top left), discharge (bottom left), soil moisture content (bottom right) between model simulation and RTHnet measurements, and map of water table depth averaged over entire simulation period (top right) • Simulation from 0000 UTC 01 May to 0000 UTC 01 Sep 2009 • Model is spun-up by running from 01 May 2008 to 01 May 2009 • Model is calibrated with in-situ measurements using “trial and error” strategy • Model time step is 1 minute and output interval is 1 hour • Results could be improved by – applying better optimization method –using locally measured bedrock depth, soil map and parameters, and vegetation cover and parameters – and adopting better physics •Land surface variables are affected by topography, soil type, and landcover type, and are correlated to groundwater table Surface energy balance (SEB) predictions Comparison of sensible heat flux (H), latent heat flux (LE), and net radiation (R n ) (from top to bottom) between model and RTHnet flux tower from 01 Aug to 01 Sep 2009 • Goals: – Develop a fully-coupled groundwater-land-surface model – Comprehensively evaluate hydrologic and surface energy balance predictions with high-frequency data at a measurement- rich site Input data Source Input data Surface elevation Field survey Soil map and parameters SSURGO Vegetation cover and parameters NLCD 2001 Precipitation, air temperature, and RH RTHnet 10-m wind speed, downward longwave radiation, downward solar radiation, and surface pressure NARR LAI, roughness length NLCD 2001 Evaluation data Discharge RTHnet outlet gauge Water table depth and soil moisture content RTHnet wells Surface heat fluxes and net radiation RTHnet flux tower Configuration of vegetation type (top left), soil type (top right) and surface elevation (bottom) of simulation domain. Grid setting for SSHO model domain. • Model domain –Total size 0.076 km 2 with a triangular irregular network of 571 grids and 318 nodes – River channel represented by 21 river segments – Uniform bedrock depth (2 m) • Model captures temporal variation of surface heat fluxes reasonably well • Model performance is limited by quality of NARR radiation products Hydrologic predictions • Model captures temporal patterns, but tends to underestimate amplitudes in water table depth and soil moisture variation • Model reproduces flood in June and low flow situations Evaluation data Input data Input data Model Predictions Calibration Tuned parameters Model Predictions Evaluation data Evaluation Untuned parameters Calibration Evaluation
Transcript of A Watershed-Scale Groundwater-Land-Surface Model · 2011-10-13 · A Watershed-Scale...
A Watershed-Scale Groundwater-Land-Surface ModelYuning Shi*, Kenneth Davis*, Christopher Duffy†, Xuan Yu†
*Department of Meteorology, the Pennsylvania State University† Department of Civil and Environmental Engineering, the Pennsylvania State University
Introduction
Model and data• A land-surface module is incorporated into the Penn StateIntegrated Hydrologic Model 2.0 (PIHM 2.0)–Fully coupled surface water, groundwater, and land-surface
components–Land-surface scheme is mainly adapted from the Noah LSM
• Susquehanna/Shale Hills Observatory (SSHO) in centralPennsylvania (0.08 km2)–Small-scale V-shaped catchment with 1st order stream–Real Time Hydrologic monitoring network (RTHnet) with an array
of land-surface and sub-surface sensors is installed in SSHO
Future Work• Questions to answer:–How does land-surface affect hydrologic predictions?–How does groundwater improve SEB prediction?
• Incorporate data assimilation module into model to assimilate in-situ measurements and optimize model parameters
• Study subsurface-land-surface interaction• Test model on different spatial scales• Evaluate model on flood/drought prediction at scales up to theJuniata River Basin (~8800 km2)
Land Surface Models- Ignore deep ground-water and lateral water flow
Groundwater models- Relatively simple evapo-transpiration scheme
Coupled models of groundwater and land surface - may yield significant improvements in short-term climate and flood/drought forecasting
Map of sensible heat flux (left) and latent heat fluxes(right) averaged over entire simulation period
Results
Simulated sensibleheat flux, latent heatflux, ground heat flux(G), and surface skintemperature (Tskin) asfunctions of watertable depth
Comparison of hourly water table depth (top left),discharge (bottom left), soil moisture content (bottomright) between model simulation and RTHnetmeasurements, and map of water table depth averagedover entire simulation period (top right)
• Simulation from 0000 UTC 01 May to 0000 UTC 01 Sep 2009• Model is spun-up by running from 01 May 2008 to 01 May 2009• Model is calibrated with in-situ measurements using “trial anderror” strategy
• Model time step is 1 minute and output interval is 1 hour
• Results could be improved by–applying better optimization method–using locally measured bedrock depth, soil map and
parameters, and vegetation cover and parameters–and adopting better physics
• Land surface variables are affected by topography, soil type, andlandcover type, and are correlated to groundwater table
Surface energy balance (SEB) predictions
Comparison of sensible heat flux (H), latent heat flux(LE), and net radiation (Rn) (from top to bottom) betweenmodel and RTHnet flux tower from 01 Aug to 01 Sep2009
• Goals:–Develop a fully-coupled groundwater-land-surface model–Comprehensively evaluate hydrologic and surface energy
balance predictions with high-frequency data at a measurement-rich site
Input data Source
Input data
Surface elevation Field surveySoil map and parameters SSURGOVegetation cover and parameters NLCD 2001Precipitation, air temperature, and RH RTHnet10-m wind speed, downward longwave radiation, downward solar radiation, and surface pressure
NARR
LAI, roughness length NLCD 2001
Evaluation data
Discharge RTHnet outlet gauge
Water table depth and soil moisture content RTHnet wells
Surface heat fluxes and net radiation RTHnet flux tower
Configuration of vegetation type(top left), soil type (top right) andsurface elevation (bottom) ofsimulation domain.
Grid setting for SSHO model domain.• Model domain–Total size 0.076 km2 with a triangular irregular network of 571
grids and 318 nodes–River channel represented by 21 river segments–Uniform bedrock depth (2 m)
• Model captures temporal variation of surface heat fluxesreasonably well
• Model performance is limited by quality of NARR radiationproducts
Hydrologic predictions
• Model captures temporal patterns, but tends to underestimateamplitudes in water table depth and soil moisture variation
• Model reproduces flood in June and low flow situations
Evaluation data
Input dataInput data
Model Predictions Calibration
Tuned parameters Model
Predictions
Evaluation dataEvaluationUntuned
parameters
Calibration
Evaluation
